A new approach to predicting spacecraft re-entry

This graphic is a probability density map overlaid on top of the Earth. LLNL researchers calculated the probability of the location where the Russian Phobos-Grunt spacecraft would re-enter the Earth's atmosphere. Image: Deborah Dennison based on calculations made by Matt Horsley

In
mid-December 2011, Lawrence Livermore National Laboratory (LLNL) received a
call from the Air Force Joint Space Operations Center (JSpOC). At the time,
LLNL scientists were working with JSpOC to upgrade their command and control
software.

But
this call was about something very different. The Russians had launched a
mission to Phobos, one of the moons of Mars, in early November, but it failed
to escape Earth's orbit. JSpOC asked if LLNL could help to predict where the
spacecraft would return to Earth.

Under
the direction of Alex Pertica, three LLNL organizations—the Space Situational
Awareness team, the Homeland-Defense Operational Planning System, and the National Atmospheric Release
Advisory Center—contributed
to this effort.

Accurate
prediction of spacecraft re-entries has long been challenging because the
forces that act to slow down the satellite are complex and dynamic. The high
altitude atmosphere is an important contributor to this force, and the
composition of this atmosphere can change rapidly with changes in the sun's
intensity. In order to model these effects correctly, it is important to
understand how these contributing forces change over time.

First,
LLNL researchers developed the software infrastructure necessary to make a
series of increasingly accurate predictions for the re-entry, and more
importantly, to quantify the uncertainty in these predictions.

With
the spacecraft orbiting the earth once every 90 min, making the uncertainty
window as small as possible was critical, but understanding exactly how big the
uncertainty window was as re-entry approached also was crucial.

Matt
Horsley recognized the need to use high-performance computing to fully
characterize these evolving uncertainties and to produce a series of
probability density distributions for the re-entry time, and he knew he had
about a month to do it.

With
the software in place, about 10 days before re-entry, data began to arrive from
the Air Force about the satellite's precise position. As re-entry approached,
Horsley's probability distributions got smaller and smaller. Horsley's final
prediction contained a probability distribution spanning about an hour, which
bracketed the actual re-entry time with a mid-point that was within 20 min of
the actual event.

Livermore worked with Sandia
National Laboratories to complete the picture of the Phobos-Grunt descent.
Horsley handled the prediction to the "pierce point," which is where
the atmosphere becomes so dense that the object begins to break up. Sandia
staff performed aerothermal modeling using Livermore trajectory data to continue the
prediction to where the spacecraft would land on Earth's surface.

GIS
expert Debbie Dennison at the Laboratory's Homeland-Defense Operational
Planning System (HOPS) aided the Air Force by creating a 3D visualization of
the probability distributions by draping them over Google Earth. In addition,
the National Atmospheric Release
Advisory Center
completed all advance planning necessary to perform plume modeling if
Phobos-Grunt's fuel tanks came down intact over land.

The
work performed by LLNL provided the Air Force with an unprecedented
understanding of the evolving range of potential re-entry times and locations
for the Phobos-Grunt satellite. In the end, the Phobos-Grunt spacecraft finally
landed in the Pacific Ocean west of Chile on Jan. 15, 2011. The final
prediction delivered by the LLNL team was made with data received from the Air
Force more than two hours before re-entry. However, the predictions could have
been even better—Air Force data available at two hours prior to re-entry was
received at LLNL too late to be incorporated into the final prediction.

The
team developed a "post-diction," which indicated that had this data
been received soon after it was available, the prediction would have resulted
in a probability distribution spanning about 20 min, with a mid-point within
just a few minutes of the actual re-entry time. Processes to enable more timely
data transfer in the future are currently under consideration by the Air Force.